Skip to main content

Qualitative Examination of Technology Acceptance in the Vehicle: Factors Hindering Usage of Assistance and Infotainment Systems

  • Conference paper
  • First Online:
Book cover HCI in Mobility, Transport, and Automotive Systems (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12791))

Included in the following conference series:

Abstract

More and more assisting and entertaining systems find their way into the cockpit [1]. But the proposed benefits of increased safety, efficiency, and comfort can only come into effect if drivers decide to use the systems. Therefore, it is essential to understand what determines drivers’ acceptance of technology in the vehicle. A lot of research addresses technology acceptance applying quantitative methods [2,3,4]. This work gives an outline on the Technology Acceptance Model (TAM) [5] and driving-related adaptations as well as the potential of qualitative research in this field. Further, we conducted a qualitative online study (N = 600) on factors influencing technology usage. We examined the reasons why drivers do not use a system although their car is equipped with it. The qualitative statements were analyzed according to Mayring [6]. The category scheme was developed inductively and compared with the TAM 3 [7]. The analyses show that 56.87% of the reported statements address usefulness and 12.57% ease of use. Seven additional categories emerged accounting for 27.85% of the statements. The results reveal what is subjectively important for drivers and enhance our understanding of barriers for technology usage in the car. The work outlines the potential of qualitative insights adding to the existing body of research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C., Winner, H.: Three decades of driver assistance systems: Review and future perspectives. IEEE Intell. Transp. Syst. Mag. (2014). https://doi.org/10.1109/MITS.2014.2336271

    Article  Google Scholar 

  2. Lee, Y., Kozar, K.A., Larsen, K.R.T.: The technology acceptance model: Past, present, and future. Commun. Assoc. Inf. Syst. (2003). https://doi.org/10.17705/1CAIS.01250

  3. Venkatesh, V., Brown, S.A., Bala, H.: Bridging the qualitative-quantitative divide. guidelines for conducting mixed methods research in information systems. MIS Q. (2013). https://doi.org/10.25300/MISQ/2013/37.1.02

  4. Wu, P.F.: A mixed methods approach to technology acceptance research. J. Assoc. Inf. Syst. 13, 172–187 (2012)

    Google Scholar 

  5. Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: Theory and results. Dissertation, MIT Sloan School of Management (1985)

    Google Scholar 

  6. Mayring, P.: Qualitative Inhaltsanalyse [28 Absätze]. Forum Qual. Soc. Res. 1 (2000). http://nbn-resolving.de/urn:nbn:de:0114-fqs0002204

  7. Venkatesh, V., Bala, H.: Technology Acceptance Model 3 and a research agenda on interventions. Decis. Sci. 39, 273–315 (2008)

    Article  Google Scholar 

  8. Adell, E.: Acceptance of driver support systems. In: Proceedings of the European Conference on Human Centered Design for Intelligent Transport Systems, pp. 475–486 (2010)

    Google Scholar 

  9. Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975)

    Google Scholar 

  10. Venkatesh, V.: Determinants of ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 11, 342–365 (2000)

    Article  Google Scholar 

  11. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Q. 27, 425–478 (2003)

    Article  Google Scholar 

  12. Park, Y., Son, H., Kim, C.: Investigating the determinants of construction professionals’ acceptance of web-based training: An extension of the technology acceptance model. Autom. Constr. (2012). https://doi.org/10.1016/j.autcon.2011.09.016

    Article  Google Scholar 

  13. Zhou, T., Lu, Y., Wang, B.: Integrating TTF and UTAUT to explain mobile banking user adoption. Comput. Hum. Behav. (2010). https://doi.org/10.1016/j.chb.2010.01.013

    Article  Google Scholar 

  14. Hu, R., Pu, P.: Acceptance issues of personality-based recommender systems. In: Proceedings on 3rd ACM Conference on Recommender Systems, pp. 221–224. ACM, New York (2009)

    Google Scholar 

  15. Buckley, L., Kaye, S.-A., Pradhan, A.K.: Psychosocial factors associated with intended use of automated vehicles: A simulated driving study. Accid. Anal. Prev. (2018). https://doi.org/10.1016/j.aap.2018.03.021

    Article  Google Scholar 

  16. Nordhoff, S., de Winter, J., Madigan, R., Merat, N., van Arem, B., Happee, R.: User acceptance of automated shuttles in Berlin-Schöneberg: A questionnaire study. Transp. Res. F Traffic Psychol. Behav. (2018). https://doi.org/10.1016/j.trf.2018.06.024

    Article  Google Scholar 

  17. Schmalfuß, F.: Acceptance of electric mobility system components and the role of real-life experience. Dissertation, Technische Universität Chemnitz (2017)

    Google Scholar 

  18. Bagozzi, R.P.: The legacy of the technology acceptance model and a proposal for a paradigm shift. J. Assoc. Inf. Syst. 8, 244–254 (2007)

    Google Scholar 

  19. Choi, J.K., Ji, Y.G.: Investigating the importance of trust on adopting an autonomous vehicle. Int. J. Hum.-Comput. Interact. (2015). https://doi.org/10.1080/10447318.2015.1070549

    Article  Google Scholar 

  20. Panagiotopoulos, I., Dimitrakopoulos, G.: An empirical investigation on consumers’ intentions towards autonomous driving. Transp. Res. Part C Emerg. Technol. (2018). https://doi.org/10.1016/j.trc.2018.08.013

    Article  Google Scholar 

  21. Xu, Z., Zhang, K., Min, H., Wang, Z., Zhao, X., Liu, P.: What drives people to accept automated vehicles? Findings from a field experiment. Transp. Res. Part C Emerg. Technol. (2018). https://doi.org/10.1016/j.trc.2018.07.024

    Article  Google Scholar 

  22. Ghazizadeh, M., Peng, Y., Lee, J.D., Boyle, L.N.: Augmenting the Technology Acceptance Model with trust: Commercial drivers attitudes towards monitoring and feedback. Proc. Hum. Factors Ergon. Soc. 56th Annu. Meeting 56, 2286–2290 (2012)

    Article  Google Scholar 

  23. Chen, C.-F., Chen, P.-C.: Applying the TAM to travelers’ usage intentions of GPS devices. Expert Syst. Appl. (2011). https://doi.org/10.1016/j.eswa.2010.11.047

    Article  Google Scholar 

  24. Osswald, S., Wurhofer, D., Trösterer, S., Beck, E., Tscheligi, M.: Predicting information technology usage in the car. In: Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI 2012. The 4th International Conference, Portsmouth, New Hampshire, 17–19 October 2012, pp. 51–59. ACM Press, New York (2012). https://doi.org/10.1145/2390256.2390264

  25. Hewitt, C., Politis, I., Amanatidis, T., Sarkar, A.: Assessing public perception of self-driving cars. the autonomous vehicle acceptance model. In: 24th International Conference on Intelligent User Interfaces (IUI 2019), pp. 518–527 (2019). https://doi.org/10.1145/3301275.3302268

  26. Venkatesh, V., Thong, J.Y.L., Xu, X.: Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 36, 157–178 (2012)

    Article  Google Scholar 

  27. Vogelsang, K., Steinhueser, M., Hoppe, U.: A qualitative approach to examine technology acceptance. In: Proceedings of the 34th International Conference on Information Systems, Milan, Italy (2013)

    Google Scholar 

  28. Zmud, J., Sener, I.N., Wagner, J.: Self-driving vehicles: determinants of adoption and conditions of usage. Transp. Res. Rec. (2016). https://doi.org/10.3141/2565-07

    Article  Google Scholar 

  29. Trübswetter, N., Bengler, K.: Why should I use ADAS? Advanced driver assistance systems and the elderly: knowledge, experience and usage barriers. In: Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design. Driving Assessment Conference, Bolton Landing, New York, USA, 17–20 June 2013, pp. 495–501. University of Iowa, Iowa City (2013). https://doi.org/10.17077/drivingassessment.1532

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dina Stiegemeier .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stiegemeier, D., Bringeland, S., Baumann, M. (2021). Qualitative Examination of Technology Acceptance in the Vehicle: Factors Hindering Usage of Assistance and Infotainment Systems. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2021. Lecture Notes in Computer Science(), vol 12791. Springer, Cham. https://doi.org/10.1007/978-3-030-78358-7_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78358-7_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78357-0

  • Online ISBN: 978-3-030-78358-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics